Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power). Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, and time division synchronous code division multiple access (TD-SCDMA) systems.
These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate on a municipal, national, regional, and even global level. An example of a telecommunication standard is LTE. LTE is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by Third Generation Partnership Project (3GPP). It is designed to better support mobile broadband Internet access by improving spectral efficiency, lowering costs, improving services, making use of new spectrum, and better integrating with other open standards preferably using OFDMA on the downlink (DL), SC-FDMA on the uplink (UL).
The demand for rich multimedia services over mobile (cellular) communication networks has been increasing over recent years. However, due to the centralized architecture of current mobile networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Despite the continuous efforts of mobile network operators (MNOs) and network equipment vendors to enhance the wireless link bandwidth by adopting sophisticated techniques at both the physical (PHY) layer and the medium access control (MAC) layers in LTE and LTE-Advanced systems, such as massive multiple-input multiple-output (MIMO) antenna schemes, carrier aggregation schemes, and coordinated multipoint (CoMP) transmission/reception schemes, the utilization efficiency of the radio spectrum is reaching its theoretical cap.
Due to the increased processing capability of mobile wireless communication devices such as smartphones, for example, in recent years, computationally intensive mobile applications such as image recognition, gaming, virtual reality, augmented reality, and speech recognition are becoming increasingly popular. Furthermore, data streaming such as video streaming of music and movie videos is popular. However, the computational load and data content requirements of these applications and data streams often leads to quality of service (QoS) issues which in turn lead to quality of experience (QoE) issues for users of the applications as well as quickly draining the mobile device batteries.
One way to address this problem utilizes computational offloading. Offloading computationally intensive tasks to resource-rich servers can reduce power consumption at the mobile device as well as reduce computing time for computationally intensive tasks leading to improved QoS and QoE. At the same time, QoS and QoE can also be improved through data prefetching. In a client-server model for delivery of content, a client typically makes a request for content to a server and the server responds with the requested content. In certain scenarios, the server is often waiting for the client device, or another device, to request the same or next content. To speed up the process, it is known to have the server prefetch certain content so that such content is ready to be served to the client device when the client requests delivery of such content. Prefetching and caching content is also effective where multiple mobile devices are requesting or predicted to request the same content such as, for example, music downloads, video streaming, etc.
U.S. Pat. No. 8,943,348 discloses a decision method which simultaneously considers computing time and power consumption for offloading computations. However, it does not address content prefetching and, when performing computational offloading, it does not take into account network conditions and network costs.
US2014/0310709 discloses techniques for temporarily and/or partially offloading mobile applications to one or more remote virtual machines in a server including establishing an application copy of a mobile application installed on a mobile device at a remote virtual machine, suspending the mobile application on the mobile device and offloading operations of the mobile application to the application copy at the remote virtual machine for a period of time. However, it does not address content prefetching and, when performing computational offloading, it does not take into account network conditions and network costs.
US2014/0379840 discloses a modified server for predictive prefetching whereby the server can predictively prefetch a second object for a client given a request from the client for a first object. However, it does not address computational offloading.
U.S. Pat. No. 8,880,652 discloses a method for predictive caching of web pages for display through a screen of a mobile computing device. A load request is received at a mobile computing device, where the load request includes a current timestamp and an address. The address points to a remote server storing a current copy of the address content. The mobile computing device determines whether there is an existing copy of the address content is pre-cached on the mobile computing device. The mobile computing device determines whether a difference between the current timestamp and a pre-cache timestamp is greater than a heuristic timeliness value. If it is, the mobile computing device pre-caches the current copy of the address content from the remove server at the address on the mobile computing device. The mobile computing device then provides the current copy of the address content for display on its screen. However, it does not address computational offloading.
X. Chen et al., “Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing,” IEEE/ACM Trans. On Networking, vol. 24, no. 5, October 2016 discloses multi-user computation offloading for mobile-edge cloud computing in a multi-channel wireless interference environment. It proposes a distributed computation offloading decision making scheme among mobile device users. Both communication and computation aspects of mobile-edge cloud computing are taken into account. However, it does not address content prefetching and user mobility within the network.
D. Liu et al. “Caching at the Wireless Edge: Design Aspects, Challenges, and Future Directions,” IEEE Communications Magazine, vol. 54, no. 9, pp. 22-28, September 2016 discloses methods to predict the popularity distributions and user preferences, and the impact of erroneous information. It discloses the two aspects of caching systems, content placement and delivery. It describes the trade-offs between spectral efficiency, energy efficiency, and cache size. However, it does not address computational offloading.
Sergey Andreev et al., “Exploring synergy between communications, caching, and computing in 5G-grade deployments”, IEEE Communications Magazine Year: 2016, Volume: 54, Issue: 8, Pages: 60-69 describes that all relevant practical factors need to be considered comprehensively to leverage the full synergy of converged communications, caching, and computing architecture, including the structure of content requesting, cost per backhaul connection and operating costs, user mobility control, requirements of running applications, and so on. Although there is some suggestion of offloading and caching, they are considered jointly.
US2015/0215816 discloses a system having a mobile application client that resides on a mobile device and is connected to a cloud server. The system is provided to analyze user content consumption and provide a prefetching schedule to the mobile device. The mobile device is configured to prefetch content partially in accordance with the schedule. However, it does not address computational offloading.
U.S. Pat. No. 8,799,480 discloses a method of prefetching content data in a wireless radio access network (RAN) for improving QoE of users, and for reducing delivery time for certain content objects. However, it does not address computational offloading.
Some factors affecting the performance of computational offloading in a mobile communications network, particularly at the edge of the mobile communications network, include computing power, storage capacity and wireless access efficiency. For example, if too many mobile devices choose to offload some of their computational load to the edge of the network simultaneously, this may generate considerable interference between the mobile devices, which may reduce the QoE for all end users of the mobile devices. Moreover, under limited resource conditions, prefetching data efficiently should also be considered when attempting to maintain or improve QoS and/or QoE as the computational offloading and content prefetching operations must operate under the same limited resource conditions, i.e. compete for the same limited resources. Some problems experienced when offloading computational load and/or prefetching content/data include service fluctuations in the mobile network, reduction in QoS/QoE of services to end users, and less than efficient utilization of network resources.
In view of the foregoing, there is a need to develop a joint offloading and prefetching scheme to address the aforementioned problems.